8. Natural language processing
Natural language processing (NLP) is a field of AI that deals with making computer systems that can understand and interpret human languages. NLP has many different applications, such as text generation, chatbots, and information extraction, among many others. One of the most popular uses of NLP is in large language models, such as ChatGPT, developed by OpenAI.
ChatGPT may be used by students to help with homework, prepare for an exam, or simply satisfy their curiosity while learning. Teachers can also use ChatGPT to prepare lesson plans and check assignments for grammar and information. As the popularity of the software has risen, more and more students are using this resource. And although it may seem like there are no downsides to this technology, many people think otherwise.
Students should not see ChatGPT as their answer to all the homework questions, and similarly, teachers should not see ChatGPT as the absolute of human knowledge. As mentioned in this study, it should be viewed more as an assistive technology that responds to societal values and needs. Other concerns also exist, such as the existence of bias, the knowledge not being current, plagiarism, its use as an aid in cheating, etc.
There are other technologies that use NLP, such as automated essay grading systems, which have been covered earlier in the article. Future developments with the use of NLP technologies should address the various concerns with the technology when being used in the education sector.
9. Predictive modeling
Similar to learning analytics, AI-powered predictive modeling deals with analyzing large amounts of data, which is then used to predict various outcomes, such as student performance. This information is valuable to teachers, parents, institutions, governments, and students as they can greatly help with the learning experience and setting benchmarks. This can help teachers offer timely guidance to students based on the student’s predicted performance and on their previous test or exam results.
Data-driven analysis is an important tool to have in education as it can improve individual student performance and give them additional support when needed, overall enriching their learning experience. It is also of value to governments for use in planning educational goals. A study on community college students used predictive modeling to identify at-risk students based on several key variables. This helped them to drive interventions to help these students.
10. Augmented and virtual reality
Immersive technologies, such as augmented reality (AR) and virtual reality (VR), have become increasingly popular over the past few years. AR is an immersive technology that overlays computer-generated content onto real-world objects, thus enhancing a user’s perception of reality. On the other hand, VR is a simulated virtual environment that the user can experience as if it were real. These technologies are used for gaming and metaverse but have huge potential in the education sector.